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Working with data

Amazon S3

Updated
September 12, 2017

As an application developer you can access the data you need from your S3 account in a few easy steps. This guide will tell you how to configure and connect to your data source and provide details about setting various permissions.

Data Source Basics

All data sources have a protocol and a label that you will use to reference your data. For instance S3 is the protocol we’ll use in this guide and the label will be automatically assigned to your data connection as a unique identifier, but you may change it later if you wish.

Configure a New Data Connection to S3

To create a new data connection first navigate to Algorithmia’s Data Portal where you’ll notice there is a panel that says ‘Add New Data Source’:

On that panel click the button that says “Add Data Source” which will bring up a panel that lets you chose between configuring a new data source for AWS S3 or Dropbox:

Select ‘Connect to Amazon S3’ and a modal window will open to configure an S3 connection. Here you will need to enter your AWS credentials. S3 authorization is done by adding your AWS Access Key ID and AWS Secret Access Key, which can me managed in IAM. AWS supports setting restrictions on access tokens by service and operation.

NOTE: While an algorithm NEVER sees credentials used to access data in S3, it is recommended that you provide an access key that:

Can only list, get, and put objects to S3 (i.e. cannot perform other operations on your account)

Can only access the paths in S3 that you want Algorithmia to access

Update Labels For Data Connections

If you would like to change the unique label that was automatically provided when you created the data connection, simply update it under “Label” and give it a unique name.

We create these unique labels because you may want to add multiple connections to the same S3 account and they will each need a unique label for later reference in your algorithm. The reason you might want to have multiple connections to the same source is so you can set different access permissions to each connection such as read from one file and write to a different folder.

Setting Path Restrictions for S3 Folder and File Access

The default path restrictions are set to allow access to all paths in your S3 account, however you may want to restrict your algorithm’s access to specific folders or files:

Access to a single file: ‘Algorithmia/team.jpg’

Access to everything in a specific folder: ‘Algorithmia/*’

NOTE: ‘Algorithmia*’ might match more than you’d like, so if you want to match a directory exactly end with a ‘/’.

Here we are setting our path restrictions to everything in the S3 bucket ‘Algorithmia’:

Setting Read and Write Access

The default access for your data source is set to read only, but you can change this to read and write access by checking the ‘Write Access’ box.

NOTE: Write access also means you can delete anything in the path you’ve specified in the previous step so be careful that you want read-write-delete access to the path you set in ‘Path Restriction’. Also, if your data source has Read/Write privileges, then an algorithm that you call also has Read/Write privileges to your data source.

Accessing your Data

Accessing your S3 data via the Algorithmia Data API is easy. Whether you’re writing your algorithm in Rust, Ruby, Python, Scala, Java or JavaScript simply import your data with a couple lines of code. With your S3 data connection now configured you can read and write data to and from it via Algorithmia’s Data API by specifying the protocol and label as your path to your data:

client = Algorithmia.client(‘YOUR_API_KEY’)

client.file(‘S3+unique_label://my_bucket/my_file.csv’).getFile().name

For example, to retrieve and print a file’s contents in Python:

importAlgorithmiaimportcsvclient=Algorithmia.client('your_api_key')defs3_data():# Get file from S3 data sourcedata_file=client.file('s3+saha://Algorithmia/test_data.csv').get()# Pass in file and pass in args required from the algorithm FpGrowthinput=[data_file,5,2]algo=client.algo('paranoia/FpGrowth/0.2.0')returnalgo.pipe(input)s3_data()

If you’re working with an algorithm that takes a file or directory as input from the Data API, you can also provide it a file or directory from one of your data sources:

algo.pipeJson({'inputFile':'s3+saha://Algorithmia/test_data.csv'})

NOTE: If you call an algorithm it can only access your data source. This means that it is NOT possible for an algorithm to read data from your S3 and write that data to an account controlled by an another algorithm author. Algorithms do NOT have direct access to any credentials associated with your data sources, and can only access data from a data source using the Algorithmia API.

Data Source Routes and Data API Routes

Once a data source connection has been created and configured, all of the Algorithmia client code for interacting with the Data API for file or directory creation, deletion and listing will function identically with a data source route and a data API route except for:

We do not support generic ACLs for data sources and the only way to update permissions for a data source is through the data portal where you created your data source connection.

If you’re implementing a new client or using cURL it is preferred to use the following URL structure:

‘/v1/connector/protocol+label/path’:

‘/v1/connector/s3+unique_label/my_bucket/foo.txt’

Algorithm support

We have tested to ensure that data source paths function in all of our Algorithmia clients, however:

Python support was added in version 1.0.4

NodeJS support was added in version 0.3.5
This means that algorithms in Python or JavaScript which were last compiled prior to 5/27/2016 might not have the most recent versions of these dependencies, and we can’t guarantee this new functionality will work on algorithms older than that. A simple recompilation of the algorithm will enable support without any code changes needed.